{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning to optimize parametric Quadratic Programming (pQP) problems using Neuromancer with a trainable correction layer for improved feasibility and optimality.\n",
    "\n",
    "\n",
    "This is an interactive notebook based on the python script [Part_6_pqp_lopoCorrection.py](./Part_6_pQp_lopoCorrection.py). \n",
    "\n",
    "We demonstrate how the NeuroMANCER toolbox can be used to solve a parametric quadratic program and implement a learned operator splitting method as a final layer to improve solutions.\n",
    "\n",
    "Problem formulation pQP:\n",
    "$$\n",
    "    \\begin{align}\n",
    "    &\\text{minimize } &&   x^2 + y^2\\\\\n",
    "    &\\text{subject to} &&  -x - y + p1 \\le 0\\\\\n",
    "    &  &&    x + y - p1 - 1 \\le 0\\\\\n",
    "    &  &&    x - y + p2 - 1 \\le 0\\\\\n",
    "    &  &&   -x + y - p2  \\le 0\\\\\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "with parameters $p1, p2$ and decision variables $x, y$.\n",
    "\n",
    "References:  \n",
    "    [Proximal Operator methods](https://web.stanford.edu/~boyd/papers/pdf/prox_algs.pdf)  \n",
    "    [Metric Learning for Operator Splitting paper](https://arxiv.org/abs/2404.00882)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install neuromancer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cvxpy as cp\n",
    "import numpy as np\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import neuromancer.slim as slim\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as patheffects\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.system import Node\n",
    "\n",
    "from neuromancer.modules import lopo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_seed = 408\n",
    "np.random.seed(data_seed)\n",
    "torch.manual_seed(data_seed);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly sample parameters from a uniform distribution: $-2\\le p_1 \\le 2 $ ;  $-2 \\le p_2 \\le 2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "nsim = 2000 # number of datapoints: increase sample density for more robust results\n",
    "# create dictionaries with sampled datapoints with uniform distribution\n",
    "p_low, p_high = -2.0, 2.0\n",
    "samples_train = {\"p1\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high),\n",
    "                    \"p2\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_dev = {\"p1\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high),\n",
    "                \"p2\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_test = {\"p1\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high),\n",
    "                \"p2\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "# create named dictionary datasets\n",
    "train_data = DictDataset(samples_train, name='train')\n",
    "dev_data = DictDataset(samples_dev, name='dev')\n",
    "test_data = DictDataset(samples_test, name='test')\n",
    "# create torch dataloaders for the Trainer\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=100, num_workers=0,\n",
    "                                            collate_fn=train_data.collate_fn, shuffle=True)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=100, num_workers=0,\n",
    "                                            collate_fn=dev_data.collate_fn, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=100, num_workers=0,\n",
    "                                            collate_fn=test_data.collate_fn, shuffle=True)\n",
    "# note: training quality will depend on the DataLoader parameters such as batch size and shuffle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# visualize taining and test samples for 2D parametric space\n",
    "a_train = samples_train['p1'].numpy()\n",
    "p_train = samples_train['p2'].numpy()\n",
    "a_dev = samples_dev['p1'].numpy()\n",
    "p_dev = samples_dev['p2'].numpy()\n",
    "plt.figure()\n",
    "plt.scatter(a_train, p_train, s=2., c='blue', marker='o')\n",
    "plt.scatter(a_dev, p_dev, s=2., c='red', marker='o')\n",
    "plt.title('Sampled parametric space for training')\n",
    "plt.xlim(p_low, p_high)\n",
    "plt.ylim(p_low, p_high)\n",
    "plt.grid(True)\n",
    "plt.xlabel('p1')\n",
    "plt.ylabel('p2')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n",
    "plt.show(block=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pQP Formulation in NeuroMANCER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primal Solution Map Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define neural architecture for the solution map\n",
    "func = blocks.MLP(insize=2, outsize=2,\n",
    "                bias=True,\n",
    "                linear_map=slim.maps['linear'],\n",
    "                nonlin=nn.ReLU,\n",
    "                hsizes=[80] * 4)\n",
    "# define symbolic solution map with concatenated features (problem parameters)\n",
    "xi = lambda p1, p2: torch.cat([p1, p2], dim=-1)\n",
    "features = Node(xi, ['p1', 'p2'], ['xi'], name='features')\n",
    "sol_map = Node(func, ['xi'], ['x'], name='map')\n",
    "# trainable components of the problem solution\n",
    "components = [features, sol_map]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective and Constraints in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "variable is a basic symbolic abstraction in Neuromancer\n",
    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
    "variable construction supports:\n",
    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
    "to visualize computational graph of the variable use x.show() method          \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "# variables\n",
    "x = variable(\"x\")[:, [0]]\n",
    "y = variable(\"x\")[:, [1]]\n",
    "# sampled parameters\n",
    "p1 = variable('p1')\n",
    "p2 = variable('p2')\n",
    "# objective function\n",
    "f = x ** 2 + y ** 2\n",
    "obj = f.minimize(weight=1.0, name='obj')\n",
    "objectives = [obj]\n",
    "# constraints\n",
    "Q_con = 100.\n",
    "g1 = -x - y + p1\n",
    "con_1 = Q_con * (g1 <= 0)\n",
    "con_1.name = 'c1'\n",
    "g2 = x + y - p1 - 1\n",
    "con_2 = Q_con*(g2 <= 0)\n",
    "con_2.name = 'c2'\n",
    "g3 = x - y + p2 - 1\n",
    "con_3 = Q_con*(g3 <= 0)\n",
    "con_3.name = 'c3'\n",
    "g4 = -x + y - p2\n",
    "con_4 = Q_con*(g4 <= 0)\n",
    "con_4.name = 'c4'\n",
    "constraints = [con_1, con_2, con_3, con_4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create penalty method loss function\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(components, loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parametric Problem Solution in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.001      # step size for gradient descent\n",
    "epochs = 200    # number of training epochs\n",
    "warmup = 100    # number of epochs to wait before enacting early stopping policy\n",
    "patience = 100  # number of epochs with no improvement in eval metric to allow before early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\n",
    "# define trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_loader,\n",
    "    optimizer,\n",
    "    epochs=epochs,\n",
    "    patience=patience,\n",
    "    warmup=warmup,\n",
    "    train_metric=\"train_loss\",\n",
    "    dev_metric=\"dev_loss\",\n",
    "    test_metric=\"test_loss\",\n",
    "    eval_metric=\"dev_loss\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 81.3269271850586\n",
      "epoch: 1  train_loss: 4.075157165527344\n",
      "epoch: 2  train_loss: 1.4033559560775757\n",
      "epoch: 3  train_loss: 1.1939805746078491\n",
      "epoch: 4  train_loss: 1.1011816263198853\n",
      "epoch: 5  train_loss: 1.0604759454727173\n",
      "epoch: 6  train_loss: 1.0673084259033203\n",
      "epoch: 7  train_loss: 1.066044807434082\n",
      "epoch: 8  train_loss: 1.110221028327942\n",
      "epoch: 9  train_loss: 1.0513293743133545\n",
      "epoch: 10  train_loss: 1.0366902351379395\n",
      "epoch: 11  train_loss: 0.9689551591873169\n",
      "epoch: 12  train_loss: 0.9624499082565308\n",
      "epoch: 13  train_loss: 1.147200584411621\n",
      "epoch: 14  train_loss: 1.1707956790924072\n",
      "epoch: 15  train_loss: 1.1377201080322266\n",
      "epoch: 16  train_loss: 1.0608768463134766\n",
      "epoch: 17  train_loss: 1.0397297143936157\n",
      "epoch: 18  train_loss: 1.0860965251922607\n",
      "epoch: 19  train_loss: 1.1022344827651978\n",
      "epoch: 20  train_loss: 1.2006560564041138\n",
      "epoch: 21  train_loss: 1.0307044982910156\n",
      "epoch: 22  train_loss: 0.9958203434944153\n",
      "epoch: 23  train_loss: 0.9325623512268066\n",
      "epoch: 24  train_loss: 1.1664785146713257\n",
      "epoch: 25  train_loss: 1.0308698415756226\n",
      "epoch: 26  train_loss: 1.0468683242797852\n",
      "epoch: 27  train_loss: 1.0878348350524902\n",
      "epoch: 28  train_loss: 0.963692307472229\n",
      "epoch: 29  train_loss: 1.0132461786270142\n",
      "epoch: 30  train_loss: 1.0538923740386963\n",
      "epoch: 31  train_loss: 1.1536695957183838\n",
      "epoch: 32  train_loss: 1.0782947540283203\n",
      "epoch: 33  train_loss: 0.9860759973526001\n",
      "epoch: 34  train_loss: 0.9989141225814819\n",
      "epoch: 35  train_loss: 1.2073699235916138\n",
      "epoch: 36  train_loss: 1.0294333696365356\n",
      "epoch: 37  train_loss: 1.0282437801361084\n",
      "epoch: 38  train_loss: 0.9600852131843567\n",
      "epoch: 39  train_loss: 0.9099134206771851\n",
      "epoch: 40  train_loss: 1.0727527141571045\n",
      "epoch: 41  train_loss: 1.0261775255203247\n",
      "epoch: 42  train_loss: 1.00835382938385\n",
      "epoch: 43  train_loss: 1.0619466304779053\n",
      "epoch: 44  train_loss: 1.0006139278411865\n",
      "epoch: 45  train_loss: 0.9422355890274048\n",
      "epoch: 46  train_loss: 0.9309259653091431\n",
      "epoch: 47  train_loss: 1.0097607374191284\n",
      "epoch: 48  train_loss: 1.0245739221572876\n",
      "epoch: 49  train_loss: 1.0047752857208252\n",
      "epoch: 50  train_loss: 1.0745904445648193\n",
      "epoch: 51  train_loss: 1.0080251693725586\n",
      "epoch: 52  train_loss: 0.9994204640388489\n",
      "epoch: 53  train_loss: 0.9641682505607605\n",
      "epoch: 54  train_loss: 0.9540258646011353\n",
      "epoch: 55  train_loss: 1.0961153507232666\n",
      "epoch: 56  train_loss: 1.0131916999816895\n",
      "epoch: 57  train_loss: 0.998551070690155\n",
      "epoch: 58  train_loss: 1.0810185670852661\n",
      "epoch: 59  train_loss: 1.100447654724121\n",
      "epoch: 60  train_loss: 0.9560567736625671\n",
      "epoch: 61  train_loss: 1.055842638015747\n",
      "epoch: 62  train_loss: 1.0562958717346191\n",
      "epoch: 63  train_loss: 0.9523385763168335\n",
      "epoch: 64  train_loss: 0.9329635500907898\n",
      "epoch: 65  train_loss: 1.0322344303131104\n",
      "epoch: 66  train_loss: 0.9736016988754272\n",
      "epoch: 67  train_loss: 0.9674342274665833\n",
      "epoch: 68  train_loss: 1.1385953426361084\n",
      "epoch: 69  train_loss: 1.143149733543396\n",
      "epoch: 70  train_loss: 0.9693502187728882\n",
      "epoch: 71  train_loss: 0.9456390142440796\n",
      "epoch: 72  train_loss: 0.992920994758606\n",
      "epoch: 73  train_loss: 0.9022445678710938\n",
      "epoch: 74  train_loss: 0.9278444051742554\n",
      "epoch: 75  train_loss: 1.075937032699585\n",
      "epoch: 76  train_loss: 0.9850112199783325\n",
      "epoch: 77  train_loss: 0.9178375005722046\n",
      "epoch: 78  train_loss: 0.8845502138137817\n",
      "epoch: 79  train_loss: 0.9196087718009949\n",
      "epoch: 80  train_loss: 0.9962003827095032\n",
      "epoch: 81  train_loss: 0.9955061078071594\n",
      "epoch: 82  train_loss: 0.9694515466690063\n",
      "epoch: 83  train_loss: 0.9572457075119019\n",
      "epoch: 84  train_loss: 0.9041846394538879\n",
      "epoch: 85  train_loss: 0.9852533340454102\n",
      "epoch: 86  train_loss: 1.0470491647720337\n",
      "epoch: 87  train_loss: 0.9584099054336548\n",
      "epoch: 88  train_loss: 0.9222000241279602\n",
      "epoch: 89  train_loss: 0.9784271121025085\n",
      "epoch: 90  train_loss: 0.93359375\n",
      "epoch: 91  train_loss: 0.9091929197311401\n",
      "epoch: 92  train_loss: 0.9077752828598022\n",
      "epoch: 93  train_loss: 0.9572671055793762\n",
      "epoch: 94  train_loss: 0.969027042388916\n",
      "epoch: 95  train_loss: 0.9942420721054077\n",
      "epoch: 96  train_loss: 1.018956184387207\n",
      "epoch: 97  train_loss: 1.0251327753067017\n",
      "epoch: 98  train_loss: 0.9525524377822876\n",
      "epoch: 99  train_loss: 0.9145650863647461\n",
      "epoch: 100  train_loss: 0.8636537790298462\n",
      "epoch: 101  train_loss: 0.9280802607536316\n",
      "epoch: 102  train_loss: 0.9972575902938843\n",
      "epoch: 103  train_loss: 0.9600769281387329\n",
      "epoch: 104  train_loss: 1.0095134973526\n",
      "epoch: 105  train_loss: 0.9513992071151733\n",
      "epoch: 106  train_loss: 0.9623956680297852\n",
      "epoch: 107  train_loss: 0.9361293911933899\n",
      "epoch: 108  train_loss: 0.9895545244216919\n",
      "epoch: 109  train_loss: 0.9307793378829956\n",
      "epoch: 110  train_loss: 0.9016415476799011\n",
      "epoch: 111  train_loss: 0.8831872940063477\n",
      "epoch: 112  train_loss: 0.9273918867111206\n",
      "epoch: 113  train_loss: 0.9214564561843872\n",
      "epoch: 114  train_loss: 0.8721583485603333\n",
      "epoch: 115  train_loss: 0.9482566714286804\n",
      "epoch: 116  train_loss: 1.0335031747817993\n",
      "epoch: 117  train_loss: 0.9994751811027527\n",
      "epoch: 118  train_loss: 0.9975937604904175\n",
      "epoch: 119  train_loss: 0.926750659942627\n",
      "epoch: 120  train_loss: 0.9428882598876953\n",
      "epoch: 121  train_loss: 0.8983103036880493\n",
      "epoch: 122  train_loss: 0.883632481098175\n",
      "epoch: 123  train_loss: 0.9055756330490112\n",
      "epoch: 124  train_loss: 0.9501389265060425\n",
      "epoch: 125  train_loss: 0.9337377548217773\n",
      "epoch: 126  train_loss: 0.8609024286270142\n",
      "epoch: 127  train_loss: 0.9436379671096802\n",
      "epoch: 128  train_loss: 0.9045463800430298\n",
      "epoch: 129  train_loss: 1.0126465559005737\n",
      "epoch: 130  train_loss: 0.9738355875015259\n",
      "epoch: 131  train_loss: 0.9020649194717407\n",
      "epoch: 132  train_loss: 0.9301192164421082\n",
      "epoch: 133  train_loss: 0.933600902557373\n",
      "epoch: 134  train_loss: 0.9271965026855469\n",
      "epoch: 135  train_loss: 0.8646056056022644\n",
      "epoch: 136  train_loss: 0.8649740219116211\n",
      "epoch: 137  train_loss: 0.8593904376029968\n",
      "epoch: 138  train_loss: 0.9358685612678528\n",
      "epoch: 139  train_loss: 1.0331107378005981\n",
      "epoch: 140  train_loss: 1.0337331295013428\n",
      "epoch: 141  train_loss: 0.9551450610160828\n",
      "epoch: 142  train_loss: 0.9643076658248901\n",
      "epoch: 143  train_loss: 0.9375149607658386\n",
      "epoch: 144  train_loss: 0.9011780619621277\n",
      "epoch: 145  train_loss: 0.8973819613456726\n",
      "epoch: 146  train_loss: 1.0179829597473145\n",
      "epoch: 147  train_loss: 1.0687930583953857\n",
      "epoch: 148  train_loss: 0.9418428540229797\n",
      "epoch: 149  train_loss: 0.9203605651855469\n",
      "epoch: 150  train_loss: 0.9420779347419739\n",
      "epoch: 151  train_loss: 1.016395926475525\n",
      "epoch: 152  train_loss: 0.9250065684318542\n",
      "epoch: 153  train_loss: 1.0072401762008667\n",
      "epoch: 154  train_loss: 0.9362794756889343\n",
      "epoch: 155  train_loss: 1.0800164937973022\n",
      "epoch: 156  train_loss: 0.9827104806900024\n",
      "epoch: 157  train_loss: 0.9476898908615112\n",
      "epoch: 158  train_loss: 0.9110327959060669\n",
      "epoch: 159  train_loss: 0.9178975820541382\n",
      "epoch: 160  train_loss: 0.9464552998542786\n",
      "epoch: 161  train_loss: 0.9059745073318481\n",
      "epoch: 162  train_loss: 1.0210264921188354\n",
      "epoch: 163  train_loss: 0.9498580694198608\n",
      "epoch: 164  train_loss: 0.9873492121696472\n",
      "epoch: 165  train_loss: 0.9347492456436157\n",
      "epoch: 166  train_loss: 0.8877863883972168\n",
      "epoch: 167  train_loss: 0.8688651919364929\n",
      "epoch: 168  train_loss: 1.0159295797348022\n",
      "epoch: 169  train_loss: 0.92530757188797\n",
      "epoch: 170  train_loss: 0.950319766998291\n",
      "epoch: 171  train_loss: 0.9809211492538452\n",
      "epoch: 172  train_loss: 0.9430979490280151\n",
      "epoch: 173  train_loss: 0.9024845361709595\n",
      "epoch: 174  train_loss: 0.8851737976074219\n",
      "epoch: 175  train_loss: 1.0426708459854126\n",
      "epoch: 176  train_loss: 1.043078064918518\n",
      "epoch: 177  train_loss: 0.9525459408760071\n",
      "epoch: 178  train_loss: 0.905072033405304\n",
      "epoch: 179  train_loss: 0.8708918690681458\n",
      "epoch: 180  train_loss: 0.9029949307441711\n",
      "epoch: 181  train_loss: 0.8943092226982117\n",
      "epoch: 182  train_loss: 0.9049498438835144\n",
      "epoch: 183  train_loss: 0.8987854719161987\n",
      "epoch: 184  train_loss: 0.9024509191513062\n",
      "epoch: 185  train_loss: 0.911601722240448\n",
      "epoch: 186  train_loss: 0.9767767786979675\n",
      "epoch: 187  train_loss: 0.9505125880241394\n",
      "epoch: 188  train_loss: 0.8923566937446594\n",
      "epoch: 189  train_loss: 0.9344365000724792\n",
      "epoch: 190  train_loss: 0.8782178163528442\n",
      "epoch: 191  train_loss: 0.8752244114875793\n",
      "epoch: 192  train_loss: 1.0095452070236206\n",
      "epoch: 193  train_loss: 0.8922348022460938\n",
      "epoch: 194  train_loss: 0.8700796961784363\n",
      "epoch: 195  train_loss: 0.8873379826545715\n",
      "epoch: 196  train_loss: 0.9236734509468079\n",
      "epoch: 197  train_loss: 1.0390346050262451\n",
      "epoch: 198  train_loss: 0.9887402653694153\n",
      "epoch: 199  train_loss: 0.8892545700073242\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train NLP solution map\n",
    "best_model = trainer.train()\n",
    "best_outputs = trainer.test(best_model)\n",
    "# load best model dict\n",
    "problem.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add On A Correction Layer\n",
    "\n",
    "To improve the feasibility and optimality of the predicted solutions from the learned solution map we add on a correction layer that applies a trainable iterative solver. We train the solver iterations to converge faster for the specific problem and problem parameters.\n",
    "\n",
    "Here we demonstrate the use of trainable operator splitting methods. The methods have been constructed for problems of the form\n",
    "\n",
    "$$\n",
    "    \\begin{align}\n",
    "    & \\min f(x)   \\\\\n",
    "    & \\text{subject to:} \\\\\n",
    "    & F_{ineq}(x) <= 0 \\\\\n",
    "    & F_{eq}(x)= 0 \\\\\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "These trainable operator splitting methods utilize proximal operators where\n",
    "$$\n",
    "prox_g(x) = \\argmin_y g(y) + \\frac{1}{2\\gamma}|| x - y ||^2_{M}\n",
    "$$\n",
    "with respect to a Metric $M$, defined by a positive definite matrix\n",
    "\n",
    "We learn a mapping from problem parameters $p$ to the best metric $M$ to improve convergence.\n",
    "\n",
    "\n",
    "The solver iterations use a second order approximation of the objective and first order approximation of the constraints. These will be exact here as the problem in this example is quadratic with linear constraints\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Detail\n",
    "\n",
    "For the methods implemented here the problem is reformulated as\n",
    "\n",
    "$$\n",
    "    \\begin{align}\n",
    "    & \\min f(x) \\\\\n",
    "    & \\text{subject to:} \\\\\n",
    "    & F(x,s) = 0 \\\\\n",
    "    & s>=0 \\\\\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "for slack variables $s$, and $F(x,s)$ defined as\n",
    "$$\n",
    "F(x,s) = \\begin{bmatrix} F_{eq}(x) \\\\ F_{ineq}(x) + s \\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Here operator splitting approaches are applied to the splitting\n",
    "\n",
    "$$\n",
    "\\min g_1(x,s) + g_2(x,s)\n",
    "$$\n",
    "\n",
    "with\n",
    "$$\n",
    "\\begin{align}\n",
    "    & g_1(x,s) = f(x) + i_{ \\{ (x,s) : F(x,s) = 0 \\} } \\\\\n",
    "    & g_2(x) = i_{ \\{ s : s>=0 \\}} \n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "where $i_{S}$ is the indicator function on set S.\n",
    "\n",
    "\n",
    "In this implementation for a given x the prox operator of $g_1$ is computed for the approximation\n",
    "$$\n",
    "f(x) + \\nabla_f(x)^T(y - x) + (y-x)^T H_f(x) (y-x) + i_{ \\{ y: F(x) + J_F(x)*(y - x) = 0 \\}}\n",
    "$$\n",
    "with $\\nabla_f(x)$ the gradient of f at x, $H_f(x)$ the hessian of f at x, and  $J_F(x)$ the Jacobian of F at x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#redefine the problem objective and constraints for compatability with the correction layer.\n",
    "\n",
    "#objective\n",
    "def f_obj(x,parms):\n",
    "    return torch.pow( x[0] ,2) + torch.pow( x[1], 2)\n",
    "\n",
    "#constraints\n",
    "def F_ineq(x,parms):\n",
    "    c_1 = -x[0] - x[1] + parms[0]\n",
    "    c_2 = x[0] + x[1] - parms[0] - 1 \n",
    "    c_3 = x[0] - x[1] + parms[1] - 1 \n",
    "    c_4 = -x[0] + x[1] - parms[1]\n",
    "    return torch.stack((c_1,c_2,c_3,c_4))\n",
    "\n",
    "#problem size\n",
    "x_dim = 2 # dimension of primal variable\n",
    "n_ineq = 4 #number of inequality constraints\n",
    "parm_dim = 2 #dimension of parameters for problem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a Trainable Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Define a metric that will be trained\n",
    "#here we define the metric to be a diagonal matrix with bounds on the diagonal to ensure it will be positive definite\n",
    "lb_P = 1.0/5.0 #upper bound of diagonal \n",
    "ub_P = 5.0 # lower bound on diagonal\n",
    "scl_lb_P = 0.005 #lower bound on scaling coefficient for diagonal\n",
    "scl_ub_P = 1.0 # upper bound on scaling coefficient for diagonal\n",
    "n_dim = x_dim + n_ineq\n",
    "metric = lopo.ParaMetricDiagonal(n_dim,parm_dim,ub_P,lb_P,scl_upper_bound=scl_ub_P,scl_lower_bound=scl_lb_P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize The Solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' \n",
    "Can choose either DR for Douglas Rachford or ADMM for Alternating Direction Method of Multipliers\n",
    "'''\n",
    "solver = 'DR'\n",
    "#solver = 'ADMM'\n",
    "\n",
    "num_steps = 15 # number of iterations to take\n",
    "\n",
    "if solver == 'DR':\n",
    "    solver = lopo.DRSolver(\n",
    "        f_obj = f_obj, \n",
    "        F_ineq = F_ineq,\n",
    "        x_dim = x_dim, \n",
    "        n_ineq = n_ineq, \n",
    "        JF_fixed=True,\n",
    "        Hf_fixed = True,\n",
    "        num_steps = num_steps,\n",
    "        metric = metric\n",
    "        )\n",
    "if solver == 'ADMM':\n",
    "    solver = lopo.ADMMSolver(\n",
    "        f_obj = f_obj, \n",
    "        F_ineq = F_ineq,\n",
    "        x_dim = x_dim, \n",
    "        n_ineq = n_ineq, \n",
    "        JF_fixed=True,\n",
    "        Hf_fixed = True,\n",
    "        num_steps = num_steps,\n",
    "        metric = metric\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remap Solution Through Correction Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sol_map = Node(func, ['xi'], ['x_predicted'], name='map')\n",
    "correction = Node(solver,['x_predicted','xi'],['x','cnv_gap'])\n",
    "components = [features, sol_map, correction]\n",
    "### ADD A CONVERGENCE PENALTY TO TRAIN SOLVER\n",
    "cnv_gap = variable(\"cnv_gap\")\n",
    "f_cnv = cnv_gap\n",
    "cnv_obj = f_cnv.minimize(weight=1e8, name='cnv_obj')\n",
    "objectives = [cnv_obj]\n",
    "constraints = []\n",
    "# create loss function\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(components, loss)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Metric For The Correction Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 57.85216522216797\n",
      "epoch: 1  train_loss: 0.39614301919937134\n",
      "epoch: 2  train_loss: 0.10252819955348969\n",
      "epoch: 3  train_loss: 0.06580999493598938\n",
      "epoch: 4  train_loss: 0.056206829845905304\n",
      "epoch: 5  train_loss: 0.05221138149499893\n",
      "epoch: 6  train_loss: 0.049075592309236526\n",
      "epoch: 7  train_loss: 0.046760257333517075\n",
      "epoch: 8  train_loss: 0.04377736151218414\n",
      "epoch: 9  train_loss: 0.042454905807971954\n",
      "epoch: 10  train_loss: 0.04118578881025314\n",
      "epoch: 11  train_loss: 0.038812071084976196\n",
      "epoch: 12  train_loss: 0.0388730950653553\n",
      "epoch: 13  train_loss: 0.03612465411424637\n",
      "epoch: 14  train_loss: 0.03684256225824356\n",
      "epoch: 15  train_loss: 0.035643357783555984\n",
      "epoch: 16  train_loss: 0.034114085137844086\n",
      "epoch: 17  train_loss: 0.03407121077179909\n",
      "epoch: 18  train_loss: 0.03349055349826813\n",
      "epoch: 19  train_loss: 0.03293275460600853\n"
     ]
    }
   ],
   "source": [
    "\n",
    "optimizer = torch.optim.AdamW(solver.parameters(), lr=1e-2)\n",
    "# define trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_loader,\n",
    "    optimizer,\n",
    "    epochs=20,\n",
    "    patience=200,\n",
    "    warmup=100,\n",
    "    train_metric=\"train_loss\",\n",
    "    dev_metric=\"dev_loss\",\n",
    "    test_metric=\"test_loss\",\n",
    "    eval_metric=\"dev_loss\",\n",
    ")\n",
    "# Train solution map\n",
    "best_model = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare to CVXPY Solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5t/0jpw47ds3rs3_fzmw9fk919w0000gn/T/ipykernel_28934/3973339004.py:33: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  cp_plot = ax[row_id, column_id].contourf(xx, yy, J, 50, alpha=0.4,cmap = mpl.colormaps['bone'],linewidth = 10)\n",
      "/var/folders/5t/0jpw47ds3rs3_fzmw9fk919w0000gn/T/ipykernel_28934/3973339004.py:43: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
      "  plt.setp(cg1.collections,\n",
      "/var/folders/5t/0jpw47ds3rs3_fzmw9fk919w0000gn/T/ipykernel_28934/3973339004.py:45: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
      "  plt.setp(cg2.collections,\n",
      "/var/folders/5t/0jpw47ds3rs3_fzmw9fk919w0000gn/T/ipykernel_28934/3973339004.py:47: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
      "  plt.setp(cg3.collections,\n",
      "/var/folders/5t/0jpw47ds3rs3_fzmw9fk919w0000gn/T/ipykernel_28934/3973339004.py:49: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed two minor releases later.\n",
      "  plt.setp(cg4.collections,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primal solution x=[0.5], y=[-0.75]\n",
      "parameter p=(-1.25, -1.25)\n",
      "primal solution Neuromancer x1=0.500011146068573, x2=-0.7499968409538269\n",
      "primal solution x=[0.5], y=[-0.5]\n",
      "parameter p=(-1.0, -1.0)\n",
      "primal solution Neuromancer x1=0.4999542534351349, x2=-0.50002121925354\n",
      "primal solution x=[0.25], y=[-0.25]\n",
      "parameter p=(-0.5, -0.5)\n",
      "primal solution Neuromancer x1=0.24998660385608673, x2=-0.2499862015247345\n",
      "primal solution x=[0.125], y=[-0.125]\n",
      "parameter p=(-0.25, -0.25)\n",
      "primal solution Neuromancer x1=0.12502336502075195, x2=-0.12499527633190155\n",
      "primal solution x=[0.], y=[0.]\n",
      "parameter p=(0.0, 0.0)\n",
      "primal solution Neuromancer x1=5.817413330078125e-05, x2=-1.601874828338623e-06\n",
      "primal solution x=[0.125], y=[0.125]\n",
      "parameter p=(0.25, 0.25)\n",
      "primal solution Neuromancer x1=0.12505045533180237, x2=0.12500010430812836\n",
      "primal solution x=[0.25], y=[0.25]\n",
      "parameter p=(0.5, 0.5)\n",
      "primal solution Neuromancer x1=0.24999910593032837, x2=0.249983549118042\n",
      "primal solution x=[0.5], y=[0.5]\n",
      "parameter p=(1.0, 1.0)\n",
      "primal solution Neuromancer x1=0.49996304512023926, x2=0.500012993812561\n",
      "primal solution x=[0.5], y=[0.75]\n",
      "parameter p=(1.25, 1.25)\n",
      "primal solution Neuromancer x1=0.5000221133232117, x2=0.7499827146530151\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "CVXPY benchmarks\n",
    "\"\"\"\n",
    "# Define the CVXPY problems.\n",
    "def QP_param(p1, p2):\n",
    "    x = cp.Variable(1)\n",
    "    y = cp.Variable(1)\n",
    "    prob = cp.Problem(cp.Minimize(x ** 2 + y ** 2),\n",
    "                        [-x - y + p1 <= 0,\n",
    "                        x + y - p1 - 1 <= 0,\n",
    "                        x - y + p2 - 1 <= 0,\n",
    "                        -x + y - p2 <= 0])\n",
    "    return prob, x, y\n",
    "\"\"\"\n",
    "Plots\n",
    "\"\"\"\n",
    "import matplotlib as mpl\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "# test problem parameters\n",
    "params = [-1.25, -1.0, -0.5, -0.25, 0.0, 0.25, 0.5, 1.0, 1.25]\n",
    "x1 = np.arange(-1.5, 1.5, 0.01)\n",
    "y1 = np.arange(-1.5, 1.5, 0.01)\n",
    "xx, yy = np.meshgrid(x1, y1)\n",
    "fig, ax = plt.subplots(3,3,sharex = True,sharey=True)\n",
    "row_id = 0\n",
    "column_id = 0\n",
    "for i, p in enumerate(params):\n",
    "    if i % 3 == 0 and i != 0:\n",
    "        row_id += 1\n",
    "        column_id = 0\n",
    "    # eval and plot objective and constraints\n",
    "    J = xx ** 2 + yy ** 2\n",
    "    cp_plot = ax[row_id, column_id].contourf(xx, yy, J, 50, alpha=0.4,cmap = mpl.colormaps['bone'],linewidth = 10)\n",
    "    ax[row_id, column_id].set_title(f'p={p}')\n",
    "    c1 = xx + yy - p\n",
    "    c2 = -xx - yy + p + 1\n",
    "    c3 = -xx + yy - p + 1\n",
    "    c4 = xx - yy + p\n",
    "    cg1 = ax[row_id, column_id].contour(xx, yy, c1, [0], colors='k', alpha=0.5)\n",
    "    cg2 = ax[row_id, column_id].contour(xx, yy, c2, [0], colors='k', alpha=0.5)\n",
    "    cg3 = ax[row_id, column_id].contour(xx, yy, c3, [0], colors='k', alpha=0.5)\n",
    "    cg4 = ax[row_id, column_id].contour(xx, yy, c4, [0], colors='k', alpha=0.5)\n",
    "    if hasattr(cg1, 'collections') and cg1.collections:\n",
    "        plt.setp(cg1.collections,\n",
    "                    path_effects=[patheffects.withTickedStroke()], alpha=0.5)\n",
    "        plt.setp(cg2.collections,\n",
    "                    path_effects=[patheffects.withTickedStroke()], alpha=0.5)\n",
    "        plt.setp(cg3.collections,\n",
    "                    path_effects=[patheffects.withTickedStroke()], alpha=0.5)\n",
    "        plt.setp(cg4.collections,\n",
    "                    path_effects=[patheffects.withTickedStroke()], alpha=0.5)\n",
    "    # Solve CVXPY problem\n",
    "    prob, x, y = QP_param(p, p)\n",
    "    prob.solve()\n",
    "    # Solve via neuromancer\n",
    "    datapoint = {'p1': torch.tensor([[p]]), 'p2': torch.tensor([[p]]),\n",
    "                    'name': 'test'}\n",
    "    model_out = problem(datapoint)\n",
    "    x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
    "    y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
    "    print(f'primal solution x={x.value}, y={y.value}')\n",
    "    print(f'parameter p={p, p}')\n",
    "    print(f'primal solution Neuromancer x1={x_nm}, x2={y_nm}')\n",
    "    # Plot optimal solutions\n",
    "    ax[row_id, column_id].plot(x.value, y.value, 'g*', markersize=10)\n",
    "    ax[row_id, column_id].plot(x_nm, y_nm, 'r*', markersize=10)\n",
    "    column_id += 1\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "plt.show(block=True)\n",
    "plt.interactive(False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution for 2000 problems via CVXPY obtained in 12.3171 seconds\n",
      "CVXPY mean constraints violation 0.0000\n",
      "CVXPY mean objective value 0.7382\n",
      "\n",
      "Neuromancer mean constraints violation no correction 0.0002\n",
      "Neuromancer mean objective value no correction 0.8378\n",
      "MSE primal optimizers no correction: 0.016542269751206266\n",
      "mean objective value discrepancy np correction: 13.50 % \n",
      "\n",
      "Solution for 2000 problems with correction obtained in 0.0216 seconds\n",
      "mean constraints violation with correction 0.0000\n",
      "mean objective value with correction 0.7382\n",
      "Solution speedup factor 569.9781\n",
      "MSE primal optimizers: 1.0437864019287642e-09\n",
      "mean objective value discrepancy: 0.00 %\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Benchmark Solution\n",
    "\"\"\"\n",
    "def eval_constraints(x, y, p1, p2):\n",
    "    \"\"\"\n",
    "    evaluate mean constraints violations\n",
    "    \"\"\"\n",
    "    con_1_viol = np.maximum(0, -x - y + p1)\n",
    "    con_2_viol = np.maximum(0, x + y - p1 - 1)\n",
    "    con_3_viol = np.maximum(0, x - y + p2 - 1)\n",
    "    con_4_viol = np.maximum(0, -x + y - p2)\n",
    "    con_viol = con_1_viol + con_2_viol + con_3_viol + con_4_viol\n",
    "    con_viol_mean = np.mean(con_viol)\n",
    "    return con_viol_mean\n",
    "def eval_objective(x, y, a1=1, a2=1):\n",
    "    obj_value_mean = np.mean(a1 * x**2 + a2 * y**2)\n",
    "    return obj_value_mean\n",
    "# Solve via neuromancer\n",
    "with torch.no_grad():\n",
    "    t = time.time()\n",
    "    samples_test['name'] = 'test'\n",
    "    model_out = problem(samples_test)\n",
    "    nm_time = time.time() - t\n",
    "x_nm = model_out['test_' + \"x\"][:, [0]].detach().numpy()\n",
    "y_nm = model_out['test_' + \"x\"][:, [1]].detach().numpy()\n",
    "x_nm_noDR = model_out['test_x_predicted'][:, [0]].detach().numpy()\n",
    "y_nm_noDR = model_out['test_x_predicted'][:, [1]].detach().numpy()\n",
    "# Solve via solver\n",
    "t = time.time()\n",
    "x_solver, y_solver = [], []\n",
    "for i in range(0, nsim):\n",
    "    p1 = samples_test['p1'][i].detach().numpy()\n",
    "    p2 = samples_test['p2'][i].detach().numpy()\n",
    "    prob, x, y = QP_param(p1, p2)\n",
    "    prob.solve(solver='OSQP', verbose=False)\n",
    "    prob.solve()\n",
    "    x_solver.append(x.value)\n",
    "    y_solver.append(y.value)\n",
    "solver_time = time.time() - t\n",
    "x_solver = np.asarray(x_solver)\n",
    "y_solver = np.asarray(y_solver)\n",
    "p1_vec = samples_test['p1'].detach().numpy()\n",
    "p2_vec = samples_test['p2'].detach().numpy()\n",
    "\n",
    "# Evaluate solver solution\n",
    "print(f'Solution for {nsim} problems via CVXPY obtained in {solver_time:.4f} seconds')\n",
    "solver_con_viol_mean = eval_constraints(x_solver, y_solver, p1_vec, p2_vec)\n",
    "print(f'CVXPY mean constraints violation {solver_con_viol_mean:.4f}')\n",
    "solver_obj_mean = eval_objective(x_solver, y_solver)\n",
    "print(f'CVXPY mean objective value {solver_obj_mean:.4f}\\n')\n",
    "\n",
    "# Evaluate neuromancer solution no DR\n",
    "nm_con_viol_mean = eval_constraints(x_nm_noDR, y_nm_noDR, p1_vec, p2_vec)\n",
    "print(f'Neuromancer mean constraints violation no correction {nm_con_viol_mean:.4f}')\n",
    "nm_obj_mean = eval_objective(x_nm_noDR, y_nm_noDR)\n",
    "print(f'Neuromancer mean objective value no correction {nm_obj_mean:.4f}')\n",
    "\n",
    "# Difference in primal optimizers\n",
    "dx = (x_solver - x_nm_noDR)[:,0]\n",
    "dy = (y_solver - y_nm_noDR)[:,0]\n",
    "err_x = np.mean(dx**2)\n",
    "err_y = np.mean(dy**2)\n",
    "err_primal = err_x + err_y\n",
    "print('MSE primal optimizers no correction:', err_primal)\n",
    "\n",
    "# Difference in objective\n",
    "err_obj = np.abs(solver_obj_mean - nm_obj_mean) / solver_obj_mean * 100\n",
    "print(f'mean objective value discrepancy np correction: {err_obj:.2f} % \\n')\n",
    "\n",
    "\n",
    "\n",
    "# Evaluate neuromancer solution\n",
    "print(f'Solution for {nsim} problems with correction obtained in {nm_time:.4f} seconds')\n",
    "nm_con_viol_mean = eval_constraints(x_nm, y_nm, p1_vec, p2_vec)\n",
    "print(f'mean constraints violation with correction {nm_con_viol_mean:.4f}')\n",
    "nm_obj_mean = eval_objective(x_nm, y_nm)\n",
    "print(f'mean objective value with correction {nm_obj_mean:.4f}')\n",
    "\n",
    "\n",
    "# neuromancer solver comparison\n",
    "speedup_factor = solver_time/nm_time\n",
    "print(f'Solution speedup factor {speedup_factor:.4f}')\n",
    "\n",
    "# Difference in primal optimizers0\n",
    "dx = (x_solver - x_nm)[:,0]\n",
    "dy = (y_solver - y_nm)[:,0]\n",
    "err_x = np.mean(dx**2)\n",
    "err_y = np.mean(dy**2)\n",
    "err_primal = err_x + err_y\n",
    "err_primal = np.mean((dx**2 + dy**2))\n",
    "rel_err_primal = np.mean((dx**2 + dy**2))/np.mean((x_solver**2 + y_solver**2 ))\n",
    "print('MSE primal optimizers:', rel_err_primal)\n",
    "\n",
    "# Difference in objective\n",
    "err_obj = np.abs(solver_obj_mean - nm_obj_mean) / solver_obj_mean * 100\n",
    "print(f'mean objective value discrepancy: {err_obj:.2f} %')\n"
   ]
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